SUMMARYHuman genital infection caused by C hlamydia trachomatis is thought to be immunologically mediated, resulting in local recruitment of lymphocyte subsets and inducing the production of cytokines. Little information is available about the role of lymphocyte recruitment and the regulation of cytokine production in the genital tract of C. trachomatis positive infertile women. We have evaluated the recruitment of lymphocyte subsets in the genital tract and production of Th1/Th2 cytokines in cervical secretions and laparoscopic specimens from the fallopian tubes of C. trachomatis positive infertile women ( n = 17) and compared them with controls, viz. C. trachomatis negative infertile women ( n = 20) using ELISA and flow cytometry. None of these patients were found to be infected either with Candida sps., bacterial vaginosis, Trichomonas vaginalis , Neisseria gonorrhoeae, Mycoplasma hominis or Ureaplasma urealyticum in the cervix. Flow cytometric analysis of cervical secretions in Chlamydia positive women revealed recruitment of both CD4 and CD8 lymphocytes to the genital tract was up-regulated and a variation in the production rates of different cytokines in cervical secretions and fallopian tube was observed. We found that the immune responses in cervical secretions were of Th0 type, since all the analysed cytokines, viz. IFN-g , TNF-a , IL-10 and IL-12 were up-regulated. As, both CD4 and CD8 cells contribute to the production of IFN-g and IL-10, these results suggest that along with CD4 cells, CD8 lymphocytes also may be important for local regulation of Th1/Th2 responses in the genital tract during C. trachomatis infection.

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Inspired by convolutional neural networks on 1D and 2D data, graph convolutional neural networks (GCNNs) have been developed for various learning tasks on graph data, and have shown superior performance on real-world datasets. Despite their success, there is a dearth of theoretical explorations of GCNN models such as their generalization properties. In this paper, we take a first step towards developing a deeper theoretical understanding of GCNN models by analyzing the stability of single-layer GCNN models and deriving their generalization guarantees in a semi-supervised graph learning setting. In particular, we show that the algorithmic stability of a GCNN model depends upon the largest absolute eigenvalue of its graph convolution filter. Moreover, to ensure the uniform stability needed to provide strong generalization guarantees, the largest absolute eigenvalue must be independent of the graph size. Our results shed new insights on the design of new & improved graph convolution filters with guaranteed algorithmic stability. We evaluate the generalization gap and stability on various realworld graph datasets and show that the empirical results indeed support our theoretical findings. To the best of our knowledge, we are the first to study stability bounds on graph learning in a semisupervised setting and derive generalization bounds for GCNN models.

Occurrence of aberrant phenotypes in childhood and adult acute leukemia (AL) differs considerably in independent studies and their association with prognostic factors is still controversial. In the present study, 214 patients with AL (106 children and 108 adults) were evaluated for the aberrant expression of CD33 in ALL (B cell and T cell) and CD3, CD5, CD7, and CD19 in AML. In B-ALL, aberrant expression of CD33 was found in 39 and 23% cases of adult and children, respectively. In T-ALL, CD33 was seen in 33% cases of adults while in children CD33 was not observed. In AML, aberrant expression of CD19 was expressed in 52 and 32% while CD7 was expressed in 14 and 15% cases of childhood and adult AML, respectively. Among FAB subtypes, aberrant expression of CD19 and CD7 was more commonly seen in M5 subtype. One adult patient (AML-M5) showed expression of CD3, CD5, and CD19. In summary, aberrant phenotype was commonly seen in adults than childhood B-ALL while in AML, aberrant phenotype was more common in children than adults. CD19 was most commonly expressed antigen followed by CD7 in both childhood and adult AML. Interestingly, aberrant phenotype was not found in childhood T-ALL; however, it was seen in 33% cases of adults. We did not find any association of aberrant phenotype with adverse prognosis factors, CD34 marker, and clinical outcome except the absence of auer rod which was found to be significantly associated with aberrant phenotype of childhood AML (P = 0.01).